
# Import Modules
import os
import sys

import json
from pathlib import Path
import numpy as np
import pandas as pd
import os
import random
import copy
import matplotlib.pyplot as plt
import pandas


import yfinance as yf
from pandas_datareader import data as pdr
from datetime import datetime
yf.pdr_override()


end = datetime.now()
start = datetime(end.year - 10, end.month, end.day)
print(start,' ', end)
stock_list = ['AMAT','LRCX','WOLF','KLAC','AAPL', 'GOOG', 'MSFT', 'AMZN']

2013-03-31 00:00:00   2023-03-31 17:57:28.045856


data = []
for stock in stock_list:
    df = yf.download(stock, start, end)
    df = df.reset_index()
    df['Label'] = stock
    df['Date'] = pd.to_datetime(df['Date'])
    data.append(df)

[*********************100%***********************]  1 of 1 completed
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[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed


data[0].head()


data[1].head()


print(len(data))

8


TechIndicator = copy.deepcopy(data)


def rsi(values):
    up = values[values>0].mean()
    down = -1*values[values<0].mean()
    return 100 * up / (up + down)


for stock in range(len(TechIndicator)):
    TechIndicator[stock]['Momentum_1D'] = (TechIndicator[stock]['Close']-TechIndicator[stock]['Close'].shift(1)).fillna(0)
    TechIndicator[stock]['RSI_14D'] = TechIndicator[stock]['Momentum_1D'].rolling(center=False, window=14).apply(rsi).fillna(0)
TechIndicator[1].tail(5)


TechIndicator[1].head(5)


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly
import plotly.express as px
import numpy as np


#Cleanup the 'Volume' column.
for stock in range(len(TechIndicator)):
    TechIndicator[stock]['Volume_plain'] = TechIndicator[stock]['Volume'].fillna(0)
TechIndicator[0].tail()


def bbands(price, length=30, numsd=3):    
    ave = price.rolling(window = length, center = False).mean()    
    sd = price.rolling(window = length, center = False).std()
    upband = ave + (sd*numsd)
    dnband = ave - (sd*numsd)
    return np.round(ave,3), np.round(upband,3), np.round(dnband,3)


for stock in range(len(TechIndicator)):
    TechIndicator[stock]['BB_Middle_Band'], TechIndicator[stock]['BB_Upper_Band'], TechIndicator[stock]['BB_Lower_Band'] = bbands(TechIndicator[stock]['Close'], length=30, numsd=3)
    TechIndicator[stock]['BB_Middle_Band'] = TechIndicator[stock]['BB_Middle_Band'].fillna(0)
    TechIndicator[stock]['BB_Upper_Band'] = TechIndicator[stock]['BB_Upper_Band'].fillna(0)
    TechIndicator[stock]['BB_Lower_Band'] = TechIndicator[stock]['BB_Lower_Band'].fillna(0)
TechIndicator[0].tail()


def Aroon_Oscillator(df, tf=25):
    aroonup = []
    aroondown = []
    x = tf
    while x< len(df['Date']):
        aroon_up = ((df['High'][x-tf:x].tolist().index(max(df['High'][x-tf:x])))/float(tf))*100
        aroon_down = ((df['Low'][x-tf:x].tolist().index(min(df['Low'][x-tf:x])))/float(tf))*100
        aroonup.append(aroon_up)
        aroondown.append(aroon_down)
        x+=1
    return aroonup, aroondown


for stock in range(len(TechIndicator)):
    listofzeros = [0] * 25
    up, down = Aroon_Oscillator(TechIndicator[stock])
    aroon_list = [x - y for x, y in zip(up,down)]
    if len(aroon_list)==0:
        aroon_list = [0] * TechIndicator[stock].shape[0]
        TechIndicator[stock]['Aroon_Oscillator'] = aroon_list
    else:
        TechIndicator[stock]['Aroon_Oscillator'] = listofzeros+aroon_list


TechIndicator[0].head()


TechIndicator[0].tail()


for stock in range(len(TechIndicator)):
    TechIndicator[stock]["PVT"] = (TechIndicator[stock]['Momentum_1D']/ TechIndicator[stock]['Close'].shift(1))*TechIndicator[stock]['Volume_plain']
    TechIndicator[stock]["PVT"] = TechIndicator[stock]["PVT"]+TechIndicator[stock]["PVT"].shift(1)
    TechIndicator[stock]["PVT"] = TechIndicator[stock]["PVT"].fillna(0)
TechIndicator[0].tail()


def abands(df):
    df['AB_Middle_Band'] = df['Close'].rolling(window = 20, center=False).mean()
    df['aupband'] = df['High'] * (1 + 4 * (df['High']-df['Low'])/(df['High']+df['Low']))
    df['AB_Upper_Band'] = df['aupband'].rolling(window=20, center=False).mean()
    df['adownband'] = df['Low'] * (1 - 4 * (df['High']-df['Low'])/(df['High']+df['Low']))
    df['AB_Lower_Band'] = df['adownband'].rolling(window=20, center=False).mean()


for stock in range(len(TechIndicator)):
    abands(TechIndicator[stock])
    TechIndicator[stock] = TechIndicator[stock].fillna(0)
TechIndicator[0].tail()


columns2Drop = ['Momentum_1D', 'aupband', 'adownband']
for stock in range(len(TechIndicator)):
    TechIndicator[stock] = TechIndicator[stock].drop(labels = columns2Drop, axis=1)
TechIndicator[0].head()


# Index the Data column for visulization
for stock in range(len(TechIndicator)):
    TechIndicator[stock].index = TechIndicator[stock]['Date']
    TechIndicator[stock] = TechIndicator[stock].drop(labels = ['Date'], axis = 1)


TechIndicator[0].head()


%matplotlib inline
fig = plt.figure(figsize=(20,25))
for i in range(len(TechIndicator)):
    ax = plt.subplot(4,2,i+1)
    ax.plot(TechIndicator[i].index, TechIndicator[i]['RSI_14D'])
    ax.set_title(str(TechIndicator[i]['Label'][0]))
    ax.set_xlabel("Date")
    ax.set_ylabel("Relative Strength Index")
    plt.xticks(rotation=30)
fig.tight_layout()


fig = plt.figure(figsize=(20,25))
for i in range(len(TechIndicator)):
    ax = plt.subplot(len(TechIndicator),1,i+1)
    ax.plot(TechIndicator[i].index, TechIndicator[i]['Volume_plain'], 'b')
    ax.set_title(str(TechIndicator[i]['Label'][0]))
    ax.set_xlabel("Date")
    ax.set_ylabel("Volume")
    plt.xticks(rotation=30)
fig.tight_layout()


plt.style.use('fivethirtyeight')

fig = plt.figure(figsize=(20,25))
for i in range(len(TechIndicator)):
    ax = plt.subplot(4,2,i+1)
    ax.fill_between(TechIndicator[i].index, TechIndicator[i]['BB_Upper_Band'], TechIndicator[i]['BB_Lower_Band'], color='grey', label="Band Range")
    # Plot Adjust Closing Price and Moving Averages
    ax.plot(TechIndicator[i].index, TechIndicator[i]['Close'], color='red', lw=2, label="Close")
    ax.plot(TechIndicator[i].index, TechIndicator[i]['BB_Middle_Band'], color='black', lw=2, label="Middle Band")
    ax.set_title("Bollinger Bands for " + str(TechIndicator[i]['Label'][0]))
    ax.legend()
    ax.set_xlabel("Date")
    ax.set_ylabel("Close Prices")
    plt.xticks(rotation=30)
fig.tight_layout()


plt.style.use('seaborn-whitegrid')
fig = plt.figure(figsize=(20,25))
for i in range(len(TechIndicator)):
    ax = plt.subplot(4,2,i+1)
    ax.fill(TechIndicator[i].index, TechIndicator[i]['Aroon_Oscillator'],'b', alpha = 0.5, label = "Aroon Oscillator")
    ax.plot(TechIndicator[i].index, TechIndicator[i]['Close'], 'r', label="Close")
    ax.set_title("Aroon Oscillator for " +str(TechIndicator[i]['Label'][0]))
    ax.legend()
    ax.set_xlabel("Date")
    ax.set_ylabel("Close Prices")
    plt.xticks(rotation=30)
fig.tight_layout()

<ipython-input-31-4628a4232332>:1: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
  plt.style.use('seaborn-whitegrid')


fig = plt.figure(figsize=(20,25))
for i in range(len(TechIndicator)):
    ax = plt.subplot(8,1,i+1)
    ax.plot(TechIndicator[i].index, TechIndicator[i]['PVT'], 'black')
    ax.set_title("Price Volume Trend of " +str(TechIndicator[i]['Label'][0]))
    ax.set_xlabel("Date")
    ax.set_ylabel("Price Volume trend")
    plt.xticks(rotation=30)
fig.tight_layout()


fig = plt.figure(figsize=(20,25))
for i in range(len(TechIndicator)):
    ax = plt.subplot(4,2,i+1)
    ax.fill_between(TechIndicator[i].index, TechIndicator[i]['AB_Upper_Band'], TechIndicator[i]['AB_Lower_Band'], color='grey', label = "Band-Range")
    # Plot Adjust Closing Price and Moving Averages
    ax.plot(TechIndicator[i].index, TechIndicator[i]['Close'], color='red', lw=2, label = "Close")
    ax.plot(TechIndicator[i].index, TechIndicator[i]['AB_Middle_Band'], color='black', lw=2, label="Middle_Band")
    ax.set_title("Acceleration Bands for " + str(TechIndicator[i]['Label'][0]))
    ax.legend()
    ax.set_xlabel("Date")
    ax.set_ylabel("Close Prices")
    plt.xticks(rotation=30)
fig.tight_layout()


# Get the stock quote
stock = 'AMAT'
df = pdr.get_data_yahoo(stock, start=start, end=end)
# Show teh data
df.tail()

[*********************100%***********************]  1 of 1 completed


plt.figure(figsize=(16,6))
plt.title('Close Price History for ' + stock)
plt.plot(df['Close'])
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD ($)', fontsize=18)
plt.xticks(rotation=30)
plt.show()


# Create a new dataframe with only the 'Close column 
data = df.filter(['Close'])
# Convert the dataframe to a numpy array
dataset = data.values
# Get the number of rows to train the model on
training_data_len = int(np.ceil( len(dataset) * .80 ))

training_data_len

2016


# Scale the data
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)

scaled_data

array([[0.00285566],
       [0.00207684],
       [0.00149273],
       ...,
       [0.69399012],
       [0.70865784],
       [0.70774923]])


# Create the training data set 
# Create the scaled training data set
train_data = scaled_data[0:int(training_data_len), :]
# Split the data into x_train and y_train data sets
x_train = []
y_train = []

for i in range(60, len(train_data)):
    x_train.append(train_data[i-60:i, 0])
    y_train.append(train_data[i, 0])
print(len(train_data))    
print(len(x_train))
print(len(y_train))
        
# Convert the x_train and y_train to numpy arrays 
x_train, y_train = np.array(x_train), np.array(y_train)

# Reshape the data
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
# x_train.shape

2016
1956
1956


from keras.models import Sequential
from keras.layers import Dense, LSTM

# Build the LSTM model
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape= (x_train.shape[1], 1)))
model.add(LSTM(64, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error',metrics=['accuracy'])

# Train the model
history = model.fit(x_train, y_train, batch_size=1, epochs=5)

Epoch 1/5
1956/1956 [==============================] - 25s 8ms/step - loss: 5.6376e-04 - accuracy: 0.0000e+00
Epoch 2/5
1956/1956 [==============================] - 15s 8ms/step - loss: 3.1521e-04 - accuracy: 0.0000e+00
Epoch 3/5
1956/1956 [==============================] - 15s 8ms/step - loss: 2.8879e-04 - accuracy: 0.0000e+00
Epoch 4/5
1956/1956 [==============================] - 16s 8ms/step - loss: 2.0220e-04 - accuracy: 0.0000e+00
Epoch 5/5
1956/1956 [==============================] - 15s 8ms/step - loss: 2.4678e-04 - accuracy: 0.0000e+00


# Create the testing data set

test_data = scaled_data[training_data_len - 60: , :]
print(len(test_data))
# Create the data sets x_test and y_test
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, 0])
    
# Convert the data to a numpy array
x_test = np.array(x_test)

# Reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1 ))

# Get the models predicted price values 
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)

# Get the root mean squared error (RMSE)
rmse = np.sqrt(np.mean(((predictions - y_test) ** 2)))
rmse

564
16/16 [==============================] - 1s 5ms/step

7.105468837684983


# Plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions
# Visualize the data
plt.figure(figsize=(16,6))
plt.title('Model for '  + stock)
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD ($)', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
plt.xticks(rotation=30)
plt.show()

<ipython-input-13-615b4a569934>:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  valid['Predictions'] = predictions


# Show the valid and predicted prices
valid.head()


valid.tail()


# Plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Prediction'] = predictions
# Visualize the data
plt.figure(figsize=(16,6))
plt.title('Prediction Results for '  + stock)
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close & Predicted Price USD ($)', fontsize=18)
plt.plot(valid['Close'])
plt.plot(valid['Prediction'])
plt.legend(['Close','Prediction'], loc='lower right')
plt.show()

<ipython-input-18-7472659437d7>:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  valid['Prediction'] = predictions


import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM


stock = 'AMAT'
dataset = pdr.get_data_yahoo(stock, start=start, end=end)

dataset = dataset.reset_index()
dataset['Date'] = pd.to_datetime(dataset.Date,format='%Y-%m-%d')
dataset.index = dataset['Date']
dataset.head()

[*********************100%***********************]  1 of 1 completed


dataset = dataset.sort_index(ascending=True, axis=0)
dataset2 = pd.DataFrame(index=range(0,len(dataset)),columns=['Date', 'Close'])

for i in range(0,len(dataset)):
    dataset2['Date'][i] = dataset['Date'][i]
    dataset2['Close'][i] = dataset['Close'][i]
    
dataset2.index = dataset2.Date
dataset2.drop('Date', axis=1, inplace = True)

dataset3 = dataset2.values 

train = dataset3[:training_data_len]
valid = dataset3[training_data_len:]
#print(train)
#print(valid)


# puts everything between (0,1)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset3)
#print(scaled_data)

x_train, y_train = [], []
for i in range(60,len(train)):
    x_train.append(scaled_data[i-60:i,0])
    y_train.append(scaled_data[i,0])
x_train, y_train = np.array(x_train), np.array(y_train)

x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))


model = Sequential()
model.add(LSTM( units=1000, return_sequences = True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(units=1000))
model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=2)

1956/1956 - 54s - loss: 0.0012 - 54s/epoch - 28ms/step

<keras.callbacks.History at 0x7f709b289f10>


test_data = scaled_data[training_data_len - 60: , :]
# Create the data sets x_test and y_test
x_test = []
#y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, 0])
    
# Convert the data to a numpy array
x_test = np.array(x_test)

# Reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1 ))

closing_price = model.predict(x_test)
closing_price = scaler.inverse_transform(closing_price)
#print(X_test.shape)

train = dataset2[:training_data_len]
valid = dataset2[training_data_len:]
valid['Predictions'] = closing_price

16/16 [==============================] - 0s 27ms/step

<ipython-input-39-f8e30692b62c>:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  valid['Predictions'] = closing_price


plt.figure(figsize=(20,10))
plt.title('Model for '  + stock)
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD ($)', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
plt.xticks(rotation=30)
plt.show()


valid.head()


valid.tail()


# Plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Prediction'] = predictions
# Visualize the data
plt.figure(figsize=(16,6))
plt.title('Prediction Results for '  + stock)
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close & Predicted Price USD ($)', fontsize=18)
plt.plot(valid['Close'])
plt.plot(valid['Prediction'])
plt.legend(['Close','Prediction'], loc='lower right')
plt.xticks(rotation=30)
plt.show()

<ipython-input-44-f35a25d10c06>:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  valid['Prediction'] = predictions

	Date	Open	High	Low	Close	Adj Close	Volume	Label
0	2013-04-01	13.48	13.49	13.27	13.36	11.490361	11026400	AMAT
1	2013-04-02	13.46	13.46	13.19	13.24	11.387155	8535600	AMAT
2	2013-04-03	13.24	13.24	13.02	13.15	11.309752	14428100	AMAT
3	2013-04-04	13.10	13.24	13.03	13.22	11.369953	7399800	AMAT
4	2013-04-05	13.02	13.22	12.91	13.20	11.352753	10349000	AMAT

	Date	Open	High	Low	Close	Adj Close	Volume	Label
0	2013-04-01	41.340000	41.419998	40.700001	40.799999	35.655560	1602800	LRCX
1	2013-04-02	40.910000	41.200001	40.490002	40.689999	35.559414	1992400	LRCX
2	2013-04-03	40.849998	41.090000	40.189999	40.470001	35.367153	2716500	LRCX
3	2013-04-04	40.439999	41.299999	40.270000	41.230000	36.031338	1898600	LRCX
4	2013-04-05	40.610001	40.959999	40.060001	40.770000	35.629326	1645200	LRCX

	Date	Open	High	Low	Close	Adj Close	Volume	Label	Momentum_1D	RSI_14D
2515	2023-03-27	508.260010	509.799988	494.679993	495.769989	495.769989	1107100	LRCX	-6.290009	69.195968
2516	2023-03-28	495.399994	496.549988	478.769989	485.079987	485.079987	1463600	LRCX	-10.690002	68.993074
2517	2023-03-29	494.540009	521.159973	492.260010	515.750000	515.750000	2072200	LRCX	30.670013	70.862550
2518	2023-03-30	523.609985	532.760010	521.960022	531.359985	531.359985	2055300	LRCX	15.609985	71.266370
2519	2023-03-31	527.640015	532.715576	525.390015	528.700012	528.700012	223027	LRCX	-2.659973	70.985876

	Date	Open	High	Low	Close	Adj Close	Volume	Label	Momentum_1D
0	2013-04-01	41.340000	41.419998	40.700001	40.799999	35.655575	1602800	LRCX	0.000000
1	2013-04-02	40.910000	41.200001	40.490002	40.689999	35.559422	1992400	LRCX	-0.110001
2	2013-04-03	40.849998	41.090000	40.189999	40.470001	35.367176	2716500	LRCX	-0.219997
3	2013-04-04	40.439999	41.299999	40.270000	41.230000	36.031334	1898600	LRCX	0.759998
4	2013-04-05	40.610001	40.959999	40.060001	40.770000	35.629337	1645200	LRCX	-0.459999

	Date	Open	High	Low	Close	Adj Close	Volume	Label	Momentum_1D	RSI_14D	Volume_plain
2515	2023-03-27	120.750000	120.980003	118.320000	118.870003	118.870003	4628200	AMAT	-0.659996	60.772359	4628200
2516	2023-03-28	118.889999	119.059998	115.580002	116.400002	116.400002	5523800	AMAT	-2.470001	61.147988	5523800
2517	2023-03-29	118.720001	121.010002	117.360001	119.849998	119.849998	7865300	AMAT	3.449997	60.986116	7865300
2518	2023-03-30	122.000000	123.379997	121.339996	122.110001	122.110001	6213100	AMAT	2.260002	61.022278	6213100
2519	2023-03-31	121.519997	122.519997	121.000000	122.190002	122.190002	626666	AMAT	0.080002	59.435375	626666

Stock Market Analysis with technical indicators and preditions¶

Setup start and end time to read the data¶

Pull data from yfinance and store Data in a dataframe. Add Label column, configure Data column.¶

Examine Data¶

Add Technical Indicators to the dataframe¶

Calculation of Relative Strength Index (RSI)¶

Relative Strength Index¶

PriceUp = Price > 0¶

PriceDown = Price < 0¶

Avg(PriceUp)/(Avg(PriceUP)+Avg(PriceDown)*100¶

Where: PriceUp(t)=1*(Price(t)-Price(t-1)){Price(t)- Price(t-1)>0};¶

PriceDown(t)=-1*(Price(t)-Price(t-1)){Price(t)- Price(t-1)<0};¶

Add Momentum_1D column for all stocks.¶

Momentum_1D = P(t) - P(t-1)¶

Calculation of Bollinger Bands¶

Calculate average closing value for given period(i.e. 30 days)¶

Calculate standard deviation of closing value for the given period(i.e. 30 days)¶

Calculate the upper band¶

Calculate the lower band¶

Calculation of Aroon Oscillator¶

Aroon-Up = (25 – Number of Days Since Recent 25-day High / 25)* 100¶

Aroon-Down = (25 – Number of Days Since Recent 25-day Low / 25) * 100¶

Calculation of Price Volume Trend¶

PVT = [((CurrentClose - PreviousClose) / PreviousClose) x Volume]¶

PVT = PVT+ PreviousPVT¶

Calculation of Acceleration Bands¶

Cleanup the tables¶

Visualization of technical indicators¶

Plot Relative Strength Index(RSI)¶

Plot Plain Volume¶

Plot Bollinger Bands¶

Plot Aroon Oscillators¶

Plot Price Volume Trend¶

Plot Acceleration bands¶

Making Predictions Using the Daily Close price¶

Let's predict AMAT stock¶

Use different approach¶

	Close	Predictions
Date
2021-04-01	141.520004	138.981064
2021-04-05	143.050003	147.576141
2021-04-06	139.539993	150.892944
2021-04-07	139.139999	147.855850
2021-04-08	139.350006	146.004211